Design of Experiments: Improved Experimental Methods in Aerospace Testing

Aerospace researchers with considerable subject-matter expertise but little formal training in the design of experiments can improve their research quality and productivity through learning statistically based experiment design.  A formal approach to experiment design ensures empirical model adequacy, quantifies variability in predictions and identifies all possible independent variable interactions. Built in safeguards protect against influences of unwanted variability.  Examples are drawn from specific studies that illustrate resource savings, quality improvements, and enhanced insights from well-designed experiments.  The class structure features short lectures followed by in-class problem solving exercises using software. Students receive note sets containing all slides presented, exercises and solutions.  The class concludes with an in-class experiment.

Learning Objectives

  • Key advantages of Design of Experiments (DOE) over traditional experiment design methods
  • How to specify the proper volume of data to enhance the probability of success and understand the concepts of inferential risk
  • Full and fractional factorial designs to efficiently quantify main effects and interactions
  • Experimental tactics to minimize and quantify unexplained variance (uncertainty)
  • Introduction to Response Surface Methods
  • Experience with experiment design software (Design Expert) including in-class exercises and an experiment

Who Should Attend

This is a course in experimental methods that is applicable to multiple disciplines. It is intended for scientists, engineers, and program, project, and line managers involved in the design and execution of experimental aerospace research, or in product/process improvement.   Undergraduate and graduate students in engineering and scientific disciplines would also benefit from exposure to the concepts presented in this course.

Course Requirements

Each student will be responsible for bringing their own laptop computer.   Software for the course is Design Expert which will be provided for training purposes.  The software operates in a Windows PC environment.


Please contact Jason Cole if you have any questions about courses and workshops at AIAA forums.

  • Introduction
    • Overview
    • History and need for improved experimental methods in Aerospace Testing 
  • Statistics
    • The t and F distributions
    • Confidence Intervals
    • Two-sample t-test
    • Power and Sample size
    • ANOVA
  • Introduction to Factorial designs
    • Main and Interaction effects
    • 2k design and analysis
    • Sequential assembly
    • Wind tunnel test example
    • Outline
  • 2k Design and Analysis Details
    • Center points and replication: pure error
    • Tests for curvature and model lack of fit
    • Regression models
  • Fractional factorial designs
    • Aliasing and design resolution
    • Foldovers
    • Wind tunnel balance calibration case study
  • Response surface methods
    • Central Composite Designs
    • Regression and use of ANOVA
    • Wind tunnel case study
    • In-class experiment
  • Introductions to restricted randomization
    • Balance calibration with temperature case study
    • Mars parachute wind tunnel test case study

Dr. Drew Landman, an AIAA Associate Fellow, is professor of Aerospace Engineering at Old Dominion University where he has developed graduate courses in DOE and RSM. He served as Chief Engineer at the Langley Full-Scale Tunnel where he developed DOE based wind tunnel test programs and force measurement system calibrations.